Swan Home Upgrades to the Latest AI Agent: Breaking Through Traditional Robot Limitations, Achieving a “Leap Moment” in Accuracy!

文章摘要：As a leading home services provider in China, Swan Home has completed a key upgrade in partnership with Udesk—evolving fully from traditional customer service robots to AI customer service agents. This upgrade precisely addresses pain points across all home service scenarios, including cleaning, childcare, and elderly care, with the problem-solving rate exceeding 80%. How exactly did they achieve this impressive result through the AI agent upgrade? We’ve summarized 4 core practices, providing replicable and clear guidance for AI agent implementation in the home services industry.

As a leading home services provider in China, Swan Home has completed a key upgrade in partnership with Udesk—evolving fully from traditional customer service robots to AI customer service agents. This upgrade precisely addresses pain points across all home service scenarios, including cleaning, childcare, and elderly care, with the problem-solving rate exceeding 80%.

How exactly did they achieve this impressive result through the AI agent upgrade? We’ve summarized 4 core practices, providing replicable and clear guidance for AI agent implementation in the home services industry.

01 Address Pain Points: Meeting the Demand for Full-Scenario AI Agent Empowerment

Facing the home services industry’s realities—diverse user needs (covering cleaning, childcare, elderly care, appliance cleaning, etc.), scattered consultation scenarios (package inquiries, order questions, rule confirmation, after-sales feedback, etc.), and vague user expressions (e.g., "looking for reliable childcare services" or "needing deep cleaning")—traditional robots only rely on keyword matching, leading to irrelevant responses. This results in low efficiency and quality for customer service teams, as well as high operational costs.

To tackle this, Swan Home clarified the core goal of "AI agent empowering full-scenario home service customer service" and broke it down into two specific directions:

• Tool upgrade: On the basis of traditional robots, upgrade and restructure a customer service AI agent adapted to all home service scenarios, breaking the capability limitations of traditional robots.
• User experience upgrade: Through the "AI + AI agent consultation" model, fully cover all service types and create an intelligent service experience characterized by "fast response, accurate answers, and simple processes."

02 Quantify Value: Securing Cooperation and Implementable Metrics

To ensure the AI agent upgrade is "scenario-adaptable and value-measurable," Swan Home focused on "practical results" to select partners and establish clear quantitative standards, prioritizing upgrade effectiveness:

• Deepen co-creation cooperation with Udesk: Building on a long-term cooperative foundation, focus on evaluating core capabilities of the customer service AI agent—such as intent recognition accuracy and order linkage adaptability. Its mature implementation experience in home service scenarios became a key consideration for deepening cooperation.
• Establish clear and verifiable measurement metrics: Set benchmarks for project value in advance—≥80% acceptance rate for regular consultations across all scenarios, ≤20% transfer-to-human rate for core scenarios, and ≥15 percentage points increase in user satisfaction—fundamentally avoiding the problem of "ambiguous AI value."

03 Precise Interaction: Unleashing the Core Effectiveness of the AI Agent

Focusing on high-frequency user consultation scenarios, Swan Home leverages the customer service AI agent’s core capabilities (such as powerful intent recognition and context memory) to break through the response limitations of traditional robots, achieving accurate and efficient end-to-end responses:

• Service package consultation: For vague expressions like "cleaning suitable for a family of three" or "which elderly care service to choose," the AI agent can accurately capture core needs. Combined with contextual information (e.g., housing area, budget) mentioned by the user earlier, it provides scenario-adaptable solutions—instead of the fixed scripts output by traditional robots.
• Order-related consultation: When users ask questions such as "changing weekend childcare service to next Friday" or "calculating service duration after renewal," the AI agent does not need to repeatedly inquire. It can identify the order subject through contextual association and answer clearly, avoiding the "irrelevant responses" issue of traditional robots.
• After-sales consultation: When users provide feedback like "missed window cleaning" or "childcare service did not meet expectations," the AI agent can connect to historical service records, understand the context of the complaint, and clearly inform the after-sales processing process and plan—making responses more targeted than traditional robots.

04 Iterative Optimization: Ensuring Continuous Adaptability of the AI Agent

To guarantee the implementation effect of the customer service AI agent, Swan Home and Udesk adopted a "steady and phased promotion" strategy. Through full-process testing and continuous optimization, they ensured service quality and business adaptability:

• Comprehensive pre-launch testing: Conduct full-scenario simulation tests before official launch, covering all types of service consultation scenarios. Record test data in detail and continuously optimize the AI agent’s conversation logic, knowledge base, and order linkage processes.
• Phased gray-scale launch: Initially launch only for 2 hours during daily peak consultation periods, focusing on monitoring core indicator performance and conducting targeted optimization overnight. Gradually expand launch hours and covered scenarios once core data stabilizes.
• Continuous iterative optimization: After launch, regularly sort out user feedback and customer service backup records, update knowledge base content (e.g., adding "notes on cleaning old houses" or "childcare standards for families with infants"), and optimize conversation guidance logic—ensuring the AI agent continuously adapts to dynamic business changes.

Conclusion: Key Insights for AI Agent Implementation in the Home Services Industry

Swan Home’s practice proves that AI agent implementation is not a gamble, but a systematic project requiring "identifying needs, quantifying value, precise interaction, and iterative optimization." For home service platforms, the key lies not in "how advanced the technology is," but in "whether it aligns with business realities and can solve core pain points."

If your enterprise is also seeking to upgrade and break through traditional customer service robots, and is confused about the upgrade direction and implementation path, you may refer to Swan Home’s 4 core practices—clarify directions based on your own situation, quantify indicators, focus on high-value scenarios, and iterate steadily. In this way, AI can truly transform from a "technical concept" to a "business growth engine."

For more information and free trial, please visit https://www.udeskglobal.com/

The article is original by Udesk, and when reprinted, the source must be indicated：https://www.udeskglobal.com/blog/swan-home-upgrades-to-the-latest-ai-agent-breaking-through-traditional-robot-limitations-achieving-a-leap-moment-in-accuracy.html